Abstract
In manufacturing processes, it is very important that the condition of the cutting tool, particularly the indications when it should be changed, can be monitored. Cutting tool condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. A unique fuzzy neural hybrid pattern recognition algorithm has been developed which combines the transparent representation of fuzzy system with the learning ability of neural networks. The algorithm has strong modeling and noise suppression ability. These leads to successful tool wear classification under a range of machining conditions.
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© 2006 Springer-Verlag Berlin Heidelberg
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Fu, P., Hope, A.D. (2006). Intelligent Classification of Cutting Tool Wear States. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_141
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DOI: https://doi.org/10.1007/11760191_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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